Application of seismic attribute analysis in fluvial seismic geomorphology
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Seismic attributes can be important predictors, either qualitative or quantitative, of reservoir geometries when they are correctly used in reservoir characterization studies. This paper discusses seismic attribute analyses and their usefulness in seismic geomorphology study of Moragot field of Pattani Basin, Gulf of Thailand. Early to Middle Miocene fluvial channel and overbank sands are the reservoirs in Pattani Basin. Due to their limited horizontal and vertical distribution, it is not always possible to predict the geometry and distribution of these sands based on the conventional seismic interpretation. This study utilized various seismic attributes, e.g., RMS amplitude analysis, spectral decomposition, semblance and dip-steered similarity, RGB blending to image the geometry and the spatial distribution of sand bodies in horizon and stratal slices at different stratigraphic intervals. Attribute analyses reveal, at shallow stratigraphic levels, RMS and semblance can successfully identify channel-shaped sand bodies and mud-filled channels associated with channel belts. On the other hand in deeper stratigraphic intervals, sand distribution can be imaged more effectively by using spectral decomposition and dip-steered similarity volumes. High-frequency spectral decomposition slices can image thin sands, and low-frequency slices can image thick sands quite effectively in deeper intervals. RGB blending of different frequency slices is particularly useful in delineating channel systems of various dimensions at deeper intervals. These images show the distribution of sands and mud-filled channels at various stratigraphic levels. The width of channel belts varies from 200 m to 3 km. These channel belts are N–S or NW–SE oriented. From the channel pattern and their dimensions, depositional environments can be predicted. Mud-filled channels identified in the horizon slices will act as a connectivity barrier between sand bodies at either side of the channel. They can also act as lateral and up-dip seal to form stratigraphic traps. The seismic attribute analyses clearly show the geometry and spatial distribution of sand bodies. Hence, this method for predicting sand body geometry might help in field development planning as well as in reducing exploration risk.
KeywordsSeismic geomorphology Seismic attributes Semblance Spectral decomposition
Recent development of three-dimensional (3D) seismic datasets enables geologists to visualize and analyze buried lands and seascapes revealed by subsurface geophysical data in a manner resembling surface geomorphology (Miall 2002; Davies et al. 2007). Seismic geomorphology is a useful technique to interpret seismic patterns for formation geomorphology of a formation, which is similar to using satellite and aerial photographs of the Earth’s surface. Seismic geomorphology has lately become an essential tool for the analysis of depositional settings of a wide range of environments. It has proven to serve as an excellent tool for the identification of key architectural elements of different depositional systems. Understanding the depositional system is essential in the hydrocarbon exploration (Davies et al. 2007). Seismic geomorphology interpretation is also a primary method in viewing, mapping subsurface geologic features, and making interpretation of structure and stratigraphy possible away from well control in petroleum exploration. Seismic attributes have been increasingly used in petroleum exploration and production and have been integrated in the seismic interpretation process. Since 1990s, the seismic attributes have been developed into many types of them such as structural attributes and stratigraphic attributes (Brown 2001; Chopra and Marfurt 2007). The fundamental seismic data type is amplitude data, but seismic attributes can reveal characteristics, which are not easily seen in amplitude data themselves. The seismic attribute technique should allow us to increase ability of geological interpretation of a formation, particularly in the thin-bed reservoir environments (Farfour and Yoon 2014). Seismic attributes typically provide information relating to the amplitude, shape, and position of seismic waveform. Seismic attribute analysis can extract information from seismic data that is otherwise hidden in the data and has been used to identify prospects, ascertain depositional environments (e.g., fluvial or deep-water channels, carbonate buildups), detect and enhance faults and fracture sets to unravel structural history, and even provide direct hydrocarbon indicators (Burnett et al. 2003; Castagna et al. 2003; Chopra and Marfurt 2007; Farfour et al. 2012).
Studying seismic geomorphology by seismic attribute analyses to identify the sand body geometry and distribution at reservoir levels in Moragot field of Pattani Basin.
To evaluate the efficacy of seismic attributes in delineating geomorphic features at different subsurface intervals.
Suggesting the best attributes to image sand body geometry and distribution in Pattani Basin and adjacent areas.
Study area and dataset
The wireline log data consist of gamma ray, resistivity, and neutron–density logs from 96 wells. Three of these 96 wells have sonic log. Therefore, these data may be more appropriate to study the seismic attributes and spectral decomposition for sand body geometry and distribution prediction.
For seismic interpretation, firstly marker horizons were identified. Then, they were interpreted with the help of seismic to well tie. Based on the interpreted marker horizons, study interval was divided into three periods. To study seismic geomorphology in each period, several stratal and horizon slices were generated from the interpreted seismic horizons.
Well-log analysis also performed to identify the variations in sand thickness in different periods. Well data were integrated with seismic attribute analysis through seismic to well tie to identify the seismic amplitude variation with sand thickness. A well-log-based depositional environment analysis was performed to infer the depositional setting in each period. This is very helpful in anticipating and analyzing features in horizon slices of different periods.
Root mean square (RMS)
Amplitude maps were generated by using RMS (root mean square) amplitude attributes along the interpreted horizons to map the sand distributions (Brown 2001). Variable small window intervals were selected along each horizon depending upon the thickness of the sands encountered at wells. This window interval ranges from 5 to 20 ms.
Semblance is a measure of trace-to-trace similarity or continuity of seismic waveform in a specified window. In this study, each point in 3D seismic volume was compared with the waveform of 12 adjacent traces over a short vertical window of 9 ms. Semblance can provide useful information about lateral and vertical stratigraphic changes and may be helpful to detect sands associated with fluvial systems (Peyton et al. 1998). In this study, semblance slices were utilized to image the fluvial reservoir sands.
Dip-steered similarity is also analogous to semblance attribute (Chopra and Marfurt 2005). It measures the trace-to-trace similarity in waveform by considering the dip. For this, a dip-steering volume was calculated, and this volume was used to dictate similarity analysis from the original seismic volume. As it takes structural dip into account, it gives a better picture of structural features in horizon slices.
RMS overlay on semblance
RMS overlay on semblance with a transparency of 50% was particularly useful for mapping sands in the shallow stratigraphic level. High-amplitude features are more apparent in this method as high amplitude conforms to high semblance value.
A zone of interest was selected containing the reservoir sand zones from B to O markers. The zone of interest is from 1300 to 2100 ms, from line 5650 to 7500, and from trace 16,000 to 17700 which was used for further spectral decomposition processing. The subset of 3D seismic volume was selected along drilled prospect.
A short temporal window (24 ms) within the zone of interest was transformed from the time domain into the frequency domain. The outputs of this process are frequencies slices generated over a time-windowed zone of interest, which is termed as the tuning cube. The vertical axis of this cube is frequency instead of time. Tuning cubes are classified into two types: amplitude and phase tuning cube. In this study, only amplitude tuning cube was used. This workflow is useful to recognize a bandpass filter or a range of frequencies that can be used to create iso-frequency amplitude volumes.
Based on the selected frequency range in step 2, iso-frequency volumes were generated through DFT by setting 24 ms window length. Another spectral decomposition technique, continuous wavelet transform (CWT), was also applied in this study area. The output of both methods is time–frequency volume. CWT uses inner products to measure the similarity between signals and provides a different approach to time–frequency analysis. This method provides fine details of the signal both in time and in frequency. By applying this method, I calculated frequency volumes for 12, 20, 38, 45, and 60 Hz. The advantage of CWT is particularly helpful in tackling problems involving signal identification and detection of hidden transients that are hard to detect, short-lived elements of a signal (Partyka et al. 1999; Chopra and Marfurt 2005).
For this study, firstly four marker horizons (B, D, K, and O) have been identified and interpreted. From seismic to well tie, it was found that sand units correspond to trough and shale corresponds to peak. Based on these markers, the study area is then divided into three periods.
Seismic attribute analysis
RMS attribute analysis
The synthetic seismogram confirms that troughs on seismic data represent sands in this area. Therefore, high-amplitude troughs are an indicator of sands or high-energy environments, whereas the low amplitudes represent shale or the homogeneous low-energy environment.
In D–K interval, high amplitude in RMS maps shows diffused pattern. High amplitudes are spread out all over the horizon slices without any distinct pattern. The northern part shows more high-amplitude features as compared to the southern part. Well-log data suggest this interval contains abundant coal.
In K–O interval, RMS can detect sands (Fig. 5) as the sands in this interval are thicker on average than the sands in other zones of interest. However, if the sand thickness is low, RMS cannot detect sand (Fig. 6).
RMS amplitudes successfully identified thick sands (above 10 m), but it is not easy to detect thin sands (less than 10 m) by using the RMS amplitude analysis.
Semblance slices up to K marker show good quality images, and it is easy to interpret channels and sands (Fig. 7a, b), but the quality of semblance images below K marker (Fig. 7d) is not as good as of semblance slices above K marker. Figure 7b depicts an example showing a meandering channel running roughly in the north–south direction. The width of channels and channel belts can be measured by using semblance horizon slices and vertical sections; for example, Fig. 8 shows approximately 470 m wide channel in the southern part and 350 m wide channel in the north.
Semblance versus dip-steered similarity
Spectral decomposition analysis
Iso-frequency volumes show that in the Moragot field, the 15 m or higher thickness sands response to low frequency (20 Hz) and high frequencies (45 Hz) are responding to thinner sands (Fig. 10).
The dimensions of the fluvial system parameters at different stratigraphic intervals
Channel belt width (m)
Channel width (m)
Fluvial system parameters have been mapped based on seismic attribute analysis from the stratal and horizon slices. Periods 1 and 3 show large meander belts with wider channels, whereas both the channel belt and channel are narrower in period 2. Sinuosity is higher in periods 1 and 3 compared to period 2.
Discussion and conclusion
Different geophysical and geological techniques were applied to map the reservoir sand distribution. Key results and conclusions of the present study are summarized as follows:
The amplitude response of CWT spectral decomposition is different for different thicknesses of sands. Low frequencies (20 Hz) show high amplitudes for thick sands (> 15 m), while higher frequencies show bright amplitudes for relatively thinner sand beds.
RMS amplitude maps are useful to detect sand distribution associated with meander belts if the sand is sufficiently thick (in this case above 10 m).
Dip-steered similarity gives more accurate delineation of channel features than semblance calculated from raw seismic volume in D to K interval.
20 Hz CWT spectral decomposition along with similarity volume successfully mapped sands and mud-filled channels in the deeper stratigraphic level. These mud-filled channels may act as barrier between two separate sand bodies and compartmentalize the reservoirs.
The author would like to thank Chevron Thailand, and Chulalongkorn University for providing all the financial, data, and logistic supports.
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